Neural net controller for noise and vibration reduction

ABSTRACT

Two neural networks are used to control adaptively a vibration and noise-producing plant. The first neural network, the emulator, models the complex, nonlinear output of the plant with respect to certain controls and stimuli applied to the plant. The second neural network, the controller, calculates a control signal which affects the vibration and noise producing characteristics of the plant. By using the emulator model to calculate the nonlinear plant gradient, the controller matrix coefficients can be adapted by backpropagation of the plant gradient to produce a control signal which results in the minimum vibration and noise possible, given the current operating characteristics of the plant.

GOVERNMENT RIGHTS

[0001] The Government has rights to the invention pursuant to governmentcontract N00019-96-C-2079 awarded by the United States Naval ResearchLaboratory.

FIELD OF THE INVENTION

[0002] The present invention relates to control systems for an active,adaptive vibration and noise attenuation system (AAVNAS). The presentinvention serves as the intelligence of an overall system that hasseveral parts. Generally, the other parts of the noise control systemare called the plant and would include the noise producing systemitself, sensors for measuring the objectionable vibration and noise, amechanism for altering the production of noise and vibration, and someparameter which can be measured independently of the noise and vibrationwhich is related to the noise and vibration production and can serve asan element in the accurate estimation of noise and vibration. Inparticular, the present invention relates to a control system usingneural networks to emulate and control the noise and vibrationcharacteristics of a nonlinear plant.

BACKGROUND OF THE INVENTION

[0003] Virtually all dynamic mechanical devices produce vibration which,when transmitted through air within audible frequency ranges, isrecognized as audible noise. Both the original vibration and theresultant audible noise have undesirable effects. Vibration in machinerycan damage or degrade the machinery's performance. Noise and vibrationperceived by people in the vicinity of the machinery may distract thosepeople and cause fatigue or other physical maladies. Consequently, aneed exists for systems and techniques which can be used to reduce noiseand vibration.

[0004] Efforts to control noise and vibration can be classified into twocategories: passive and active. Passive noise control techniques aredistinguishable from active in that passive techniques are arranged toabsorb energy from the plant or, by reason of tuned mounts, isolate thevibrating machinery and thus do not add any energy to the plant, i.e.the system being controlled. Many prior attempts to control noise andvibration utilized passive techniques such as mufflers orsound-absorbing insulation. However, passive noise and vibration controltechniques approach practical limits in terms of cost and many othercharacteristics versus effectiveness. Further significant reductions innoise and vibration levels usually require advances in thestate-of-the-art active control technology.

[0005] Active techniques seek to analyze the noise and vibration thatthe plant produces and then reduce the effects by either activelyaltering the characteristics of the system or by inducing acoustic waveinterference accomplished by emitting noises/vibrations at specifictime-delayed and phased-reversed frequencies in order to cancel out thenoise and vibration from the plant. A more detailed explanation of thephysics behind noise cancellation is given in an article entitled “APrimer on Active Sound and Vibration Control” written by Larry J.Eriksson which appeared on page 18 of the February, 1997, issue of“Sensors”.

[0006] One method known in the art is to measure the noise and vibrationdisturbances at locations where cancellation is desired and to feed thisinformation back into an active controller which then makesalteration/cancellation adjustments to reduce the noise and vibrationdisturbances. Feedback systems tend to be effective when the time delaythrough the controller actuator and sensors is kept to a minimum.

[0007] Another method is to place a reference sensor as close aspossible to the vibration/noise disturbance producing source in additionto the measurements of the noise and vibration disturbances at locationswhere cancellation is desired. Such a sensor is referred to as areference sensor and allows the use of feed-forward algorithms.Feed-forward algorithms such as the Filtered-X Least Mean Squares (LMS)algorithm minimize the measured disturbance signals using a gradientdescent algorithm to adapt the coefficient of a FIR (Finite ImpulseResponse) filter. With feed-forward systems, the FIR filter coefficientsare updated so that the transfer function from the disturbance source tothe disturbance signals where cancellation is desired, is equal to thenet transfer function from the source through the reference sensor, FIRfilter, and actuator to the same disturbance signals. The adaptivealgorithm computes a FIR filter that best equalizes these two paths.U.S. Pat. No. 5,332,061 issued to Kamal Majeed, on Jul. 26, 1994,discloses one such system used to attenuate vibrations in a vehiclegenerated by an internal combustion engine. These algorithms areeffective when the reference sensors are coherent with the error signalsand have a small time delay with respect to the source and when thesystem controlled is linear. The Filtered-X LMS algorithm is describedin the textbook “Adaptive Signal Processing by Bernard Widrow and SamuelStearns© 1985, Prentice-Hall Inc., ISBN: 0-13-004029-0”.

[0008] Few practical implementations of nonlinear active controllershave been realized. Nonlinear active control systems are required whenthe actuators and/or the plant exhibit nonlinear dynamics. Neuralnetworks are one method known in the art to model and control thebehavior of nonlinear systems. A neural net plant emulator is firsttrained to identify the nonlinear plant behavior. Then, the neural netcontroller is trained in real time using the results of the emulator tocontrol the actual noise and vibration disturbance signals. There aremany publications on the training of neural nets such as backpropagation(with or without momentum), conjugate gradient, quasi-Newton algorithmsand nonlinear Kalman Filtering. One such publication is NeuralNetworks—A Comprehensive Foundation by Simon Haykin, ©1994, MacmillanPublishing Company, ISBN: 0-02-352761-7.

[0009] U.S. Pat. No. 5,434,783, issued to Chimnoy Pal, on Jul. 18, 1995,discloses a system incorporating neural networks for use in cancelingnoise and vibration in an automobile. The Pal patent discloses a systemusing two neural networks. The “identification” neural net models thebehavior of the plant being controlled. The “controller” net calculatesthe actuator command signals to reduce the automobile interior noise andvibrations of the vehicle body panel. The neural net architectureproposed in this patent includes feedback of the outputs from the neuralnet (controller or emulator) through ARMA(Auto-Regressive-Moving-Average) models to capture the temporal dynamicbehavior of the plant. The output filtered signals from the ARMA modelsare then used as the inputs to the neural nets. In addition, feedbackcoupling exists from the emulator output, also filtered by alternateARMA models to the controller inputs.

[0010] U.S. Pat. No. 5,386,689, issued to Daniel J. Bozich, on Feb. 7,1995, discloses a system similar to Pal, utilizing dual neural networksto control actively vibration in gas turbine engines. This patentdiscloses the use of two neural nets, an emulator and a controller, toreduce the vibration and noise generated by a gas turbine engine, usingactuators and sensors. The emulator in the Bozich patent is used toprovide compensation to the neural net controller by using an ideasimilar to the Fx-LMS algorithm. A reference signal is passed in afeed-forward manner through the emulator to provide a filtered referencesignal, which is then used to update the neural net controller weights.The Fx-LMS approach, and hence the approach of the Bozich patent, bothassume that the plant (as represented by the emulator) and thecontroller are interchangeable and this in general is true for linearsystems and may be possibly applicable for moderately linear systems.

[0011] The flow of a fluid over a surface is one situation in whichnoise and vibration can occur. Specific examples of this are the bladesof a rotorcraft spinning through the air or the blades of a propeller orimpeller spinning in water.

[0012] A substantial body of research into the noise and vibrationgenerated by helicopter rotors exists.

[0013] A helicopter emits a substantial amount of noise as it flies overan area. Noise and vibration levels within the helicopter cabin andthroughout the airframe can also be significant. The external noiseradiated from a helicopter can be generally classified into three areas:loading, thickness and blade-vortex interaction (BVI).

[0014] Loading noise results from the rotation of the blades that arecreating lift to keep the helicopter airborne. This rotational movementof lift generates noise that propagates perpendicular to the rotorplane, namely down toward the ground directly beneath the helicopter.

[0015] Thickness noise also results from the blades rotating around themain rotor shaft, but in contrast is independent of the lift on theblades. Thickness noise results from the pressure disturbances createdas a blade passage causes the air to displace and then return to itsinitial state. The thicker the blades, the more displacement, thus theterm thickness noise. These air displacements result in pressurefluctuations that result in radiated noise. Thickness noise radiates inthe plane of the rotor, and thus projects ahead of and behind thehelicopter. The blade velocity (as noted by the Mach number) alsoimpacts the amount of thickness noise, the faster the blades motion, thegreater the noise generated. As the relative speed of the air displacingaround the blade approaches the speed of sound (sonic or Machnumber=1.0), the magnitude of noise rises sharply (i.e. high-speedthickness noise). When regions of the displaced airflow exceed sonicvelocities, the flow is referred to as delocalized, and a great increasein sound levels and radiation is observed. In this situation, the noiseis referred to as High Speed Impulsive (HSI) noise. The highest velocityflow is observed on the advancing side of the rotor, and thus thehighest thickness noise levels propagate ahead of the helicopter.

[0016] As used in this application, HSI noise should be understood toalso cover the more general thickness type of noise which may or may notoccur at the time that HSI noise occurs. Since the highest Mach numbersare on the advancing side of the rotors, these noise sources propagateahead of the aircraft and can result in the helicopter's detection overa battlefield by threat acoustic sensors and mines. Reductions in bladethickness and rotor RPM, thus Mach number, can lead to significantreductions of the noise levels that propagate forward of the helicopterin the plane of its rotor blades, thus reducing the detectability rangeof the helicopter. However, reductions in blade thickness and tip speedalso degrade rotor performance. The addition of active controltechnology to a rotor with reduced blade thickness and lower rotor RPMmay restore or improve the helicopter's performance while yieldingsignificant reductions in noise propagation and thus detectiondistances.

[0017] BVI noise is related to the close interaction of the main rotorblades with the wake vortex elements generated at the ends of thespinning main rotor blades. These interactions increase the far-fieldground noise emanating from the helicopter. BVI noise dominates thenoise levels when the helicopter is descending or when the helicopter isflying in a terrain-following flight path—sometimes referred to as napof the earth (NOE) flight profiles. BVI noise is predominantly directeddown toward the ground, i.e., perpendicular to the plane of the rotorblades. Such ground noises are undesirable in civilian contexts becausethey are objectionable to populace in the surrounding area. Such groundnoises are also undesirable in military contexts because the noise makesthe helicopter more detectable, which makes the helicopter morevulnerable to enemy action. In a military context, a reduction in BVInoise levels during descent or terrain-following flight would greatlyenhance the success of missions requiring flight at low altitude inorder to evade radar detection, while avoiding detection by acousticsensors which might trigger anti-helicopter, explosive mines.

[0018] These wake vortex elements that lead to BVI noise are also partof a more complex rotor wake structure that varies in time and spaceover the entire rotor disk. This entire wake structure is alsoresponsible for generating vibratory loads on the rotor system which aretransferred through the blades and hub into the airframe and result inundesirable vibrations in the helicopter cockpit and cabin areas.

[0019] Open loop aerodynamic simulations have shown that actuating atrailing edge flap on each rotor blade to introduce flap motions at ‘Nper rev’ can reduce BVI noise and vibration. For example, a 2 per revharmonic signal is twice the speed of rotation of the main rotor of theaircraft and for a rotor rotating at five revolutions per second (300RPM), the 2 per rev is a 10 Hz harmonic input signal. The flap istypically driven with a superposition of 2 per rev through 5 per revharmonic input signals. One published paper (“23rd European RotorcraftForum in Dresden, Germany, Sep. 16-18, 1997: Individual blade control byservo-flap and blade root control-a collaborative research anddevelopment programme”, written by D. Schimke, P. Janker, A. Blaas, R.Kube, G. Schewe, Ch. Kebler), describes the use of superimposedharmonics of a base frequency, P, of vibration and sound used to effectcancellation of that vibration and sound. The superimposed harmonicsinclude the 2P through 5P harmonics.

[0020] Researchers have also determined that a pressure transducerlocated on the leading-edge of the blade, at an appropriate blade spanlocation, would correlate well with the far-field noise. One suchpublication (“19'th European Rotorcraft Forum, Cenobbio, Italy, Sept14-16, 1993: Experimental Results of the European Helinoise AeroacousticRotor Test”, by W. R. Splettstoesser, G. Niesl, F. Cenedese, F. Nitti,D.G. Papanikas) presents experimental results correlating blade pressureand accoustic characteristics. Experimental noise data in this referenceis further evaluated in terms of bandpass levels from 2P-10P comprisingthe thickness and high speed noise and 6P-40P comprising BVI. Therefore,the output of such a transducer can be used as a noise error sensor fora closed-loop controller for controlling flap position. Theaforementioned Schminke paper also discusses the use of pressuretransducers at the rotor blades, and the use of wavelet transformationfilters to extract the BVI noise signature for closed loop control.

[0021] U.S. Pat. No. 5,588,800, issued to Bruce D. Charles, on Dec. 31,1996, discloses a system for manipulating a flap on a rotor blade toreduce the noise and vibration generated by the rotor. This systemalters the flaps in predetermined manners based on the relative angularposition of the rotor blade at any given time. The Charles controlsystem is active to the extent that it varies its output depending onthe rotor blade position, but does not include a controller to providereal time learning. Without such a controller, the Charles system isunable to optimally adapt its flap control manipulations and providemaximum reduction of noise and vibration emissions based on actualmeasurements of noise and vibration.

[0022] The prior art lacks a comprehensive scheme for actively,adaptively controlling nonlinear noise and vibration by estimating andmeasuring what noise and vibration outputs will occur based on stimulithat relate to the generating noise and vibration source and thenadapting the controlling mechanism to reduce the plant measured noiseand vibration disturbances at the desired locations.

SUMMARY OF THE INVENTION

[0023] The present invention supplies the control system necessary tomodel the plant, estimate and measure noise and vibration states, directthe alteration of noise-vibration-generating mechanisms, evaluate itsnoise-vibration-eliminating performance and adjust its directions basedupon detected errors in its plant model.

[0024] The present invention is a noise and vibration control systemassociated with a plant which includes one or more portions ofmechanical equipment involved in producing or measuring noise andvibration. The present invention filters and quantifies the noise andvibration generated by the plant. The control system incorporates anemulator neural network used to model the relationship between thequantified noise and vibration measurements and one or more stimulirelated to the plant generating the noise and vibration. A second neuralnetwork, the controller, uses a reference signal to generate a noise andvibration correction signal which is passed to some means for alteringthe noise and vibration generated by the plant. The emulator thenmeasures the effectiveness of the correction signal in reducing noiseand vibration. The emulator calculates the gradient of the error in itsplant model. This gradient is passed to the controller to adapt thecalculations used to generate the correction signal. Over time, theparameters within the controller are adapted to produce an optimaladjustment signal reducing the noise and vibrations generated by theplant. The input signals to the emulator and controller are stored in aplurality of time-based filters.

[0025] One specific application of the present invention is to controlthe noise and vibration produced by the blades of a rotorcraft. Thenoise and vibration is measured by sensors mounted on the rotor bladesand throughout the rotorcraft. These sensed signals are then filteredand quantified. An emulator neural network models the relationshipbetween the quantified signals and various stimuli related to the motionof the rotorcraft including the angular position of the rotor relativeto the body and the forward velocity of the rotorcraft. A controllerneural network uses a reference signal and the current flight regime ofthe rotorcraft to generate an adjustment signal. This adjustment signalcontrols flaps located on the blades of the rotorcraft. By adjustingthese blade flaps as the blade rotates around the rotor, the controlleris able to alter the flow of air across the blades and, thus, alter thenoise and vibration generated by that airflow across the blades. Theemulator neural network estimates the noise and vibrations resultingfrom the adjustment signals. These estimates are compared against actualnoise and vibration measurements to develop a error gradient in theplant model. This error gradient is then used to adapt the parametersthat the controller neural network used to generate the originaladjustment signal. As this adjustment occurs during each execution cycleof the controller and emulator, the controller neural network is adaptedtoward parameters which result in adjustment signals minimizing thenoise and vibrations generated by airflow across the rotor blades.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026]FIG. 1 illustrates a sample environment in which an adaptiveactive noise control system according to the present invention isapplicable to a helicopter system;

[0027]FIG. 2 illustrates a sample environment in which an adaptiveactive noise control system according to the present invention isapplicable to a ship-born system;

[0028]FIG. 3 illustrates a sample environment in which an adaptiveactive noise control system according to the present invention isapplicable to a fixed-wing aircraft;

[0029]FIG. 4 is an overview of the logic of the present invention andhow the invention is arrange in relation to other components of a noiseand vibration reduction system;

[0030]FIG. 5 illustrates a generalized neural network which forms thebasis for the control system of the present invention;

[0031]FIG. 6 illustrates the arrangement of an emulator neural networkas the emulator is being trained to model the plant;

[0032]FIG. 7 illustrates how the components of the a sensed noise andvibration preprocessing unit convert the sensed analog noise intodigital form for use in controlling noise and vibration;

[0033]FIG. 8 illustrates the use of a digital Hilbert Transform FiniteImpulse Response (FIR) filter to generate the envelope of the noisesignal;

[0034]FIG. 9 illustrates the emulator during the control mode after theemulator has been trained to model the associated plant;

[0035]FIG. 10 illustrates a reference signal generator and describes thetype of signals generated by the preferred embodiment of the referencesignal generator;

[0036]FIG. 11 illustrates the controller neural network during thecontrol mode; and

[0037]FIG. 12 illustrates the use of a single control signal to controlmultiple devices which alter the flow of fluid over a surface.

DETAILED DESCRIPTION

[0038] Sample Applications

[0039] Referring now to FIG. 1, there is shown a system for controllingthe noise and vibration generated by the rotors of a helicopter similarto that disclosed in the Charles U.S. Pat. No. 5,588,800 patent. Thehelicopter 40 has a number of rotors blades 42, each with a trailingedge flap 44. The flaps 44 can be actuated so as to result in increasesand decreases in the lift of the blade as the blade rotates around itsdrive shaft 46. The actuations of the flaps are timed so as to reducethe overall noise and vibration of the system.

[0040] Noise sensors 48 in the form of pressure transducers mounted onat least one blade and vibration sensors 49 in the form ofaccelerometers mounted within the cabin 50 measure the effectiveness ofthe reduction efforts. The various sensors 48 and 49 deliver their errorsignals through connections 51 and 52 to a control system 53 which thencontrols the positions of the flaps 44 through a control channel 54 soas to minimize the noise and vibration generated by the blades of thehelicopter. In this system, the term “plant” is used to refer to theassemblage of the rotor blades 42, an actuator 55, a hydraulic linelinkage 56, the flaps 44, and the noise and vibration sensors 48 and 49.In this system, the instantaneous angular position of each rotor bladeis calculated as it rotates with drive shaft 46. Noise and vibrationproduction is related to the instantaneous angular position of eachblade. Therefore, the instantaneous angular position of the blade is aparameter measured independently of the noise and vibration and is usedduring the training of the neural net emulator and also in the controlmode to provide additional input information to the net to allowestimation of noise and vibration as a function of blade azimuthposition. Therefore, the flap 44 actuations occur while the rotor bladeis traveling through predetermined regions of the rotor blade's path inorder to minimize the noise and vibration generated by the blade in thatangular position. Furthermore, additional flight input signals, such asthe helicopter forward speed and payload, can also be used to provideflight condition information during the training and learning period ofthe neural net emulator and controller. As used in this application, theterm payload indicates the total weight, including passengers and cargo.These input signal(s) allow the emulator and controller to operate overa wider flight regime.

[0041] During the control mode operation, the emulator uses the inputflight condition signal(s), the blade position and the flap controlsignal to effectively estimate the noise and vibration at differentflight conditions, thus reducing the time required for re-training theemulator at each operational point in the flight regime. The controllercan also use the flight condition signals to retrieve a previouslycalculated flap control solution and initialize the control algorithm toprovide a quick convergence to the optimum solution since the previous(initial) estimate of the flap control is close to the optimum solution.This retrieval could be done from any type of computer based storagemedium such as disk drives, tapes or memory chips. The retrieval couldalso be performed by executing an algorithm developed to provide theoptimum solution based on input parameters. Note that theaerodynamic-altering effect achieved by actuating the flaps 44 couldalso instead be achieved by twisting the blades 42 around theirlongitudinal axes such as the axis 57 elastically or in a rigid manner.

[0042] Referring now to FIG. 2, there is shown a system for controllingthe noise and vibration generated by the undesired interaction of two ofthe four propeller screws of a ship. The ship, having a hull 60, has twoscrew propellers 62 and 64 on each side each with fixed pitch orvariable-pitch propeller blades 66 and 68. Part of the wash or turbulentwake of the forward propeller 62 will, at least at some speed, strikethe blades 68 of the aft propeller 64. That wash or wake from theforward propeller can interact with the blades of the aft propeller 64to generate vibration which can be transferred to the bearings (notshown) of the shaft 69 of the aft propeller. Damage can be done to thoseshaft bearings, and the vibration can be transferred to the ship's hull60.

[0043] The blade pitch of at least the second or aft-most of the twopropellers 64 can be altered so as to change the flow of water flowingover that aft propeller 68. The angular position of the drive shaft 67of the forward propeller 62 can be measured and used to estimate thefluctuations in fluid flow over the aft propeller 64 which result fromthe wash of the forward propeller 62. The pitch alterations of aftpropeller 64 are timed so as to reduce noise-and-vibration-producingeffect that the wake or wash of the first or forward propeller 62 has onthe aft propeller 64.

[0044] Noise sensors 70 mounted on the propeller blades and vibrationsensors 71 mounted within the ship's hull 60 deliver their error signalsthrough connections 72 and 73 to a control system 74 which then controlsthe pitch of the blades 68 of the second propeller 64 through a controlchannel 75 so as to minimize the noise and vibration generated. Theangular position and pitch of the blades 66 of the first propeller 62can also be used as indicative input parameters measured independentlyof the noise and vibration. Additionally, the speed of the ship throughthe water also effects the noise and vibration generated by bothpropellers and is a parameter measured independently. In this system,the plant is defined as the propellers 62 and 64, the blades 66 and 68,a hydraulic line linkage (not shown) necessary to drive the pitchvariations, the noise 70 and vibration sensors 71.

[0045] Referring now to FIG. 3, there is shown a system for controllingthe noise and vibration generated in the tail of an airplane as the wakeof the airplane's propellers flows over an elevator surface of theairplane. The airplane 80 has one or more propellers 82 with a pluralityof blades 84. The rotational angular position of the propeller blades 84can be measured so as to estimate the resultant airflow wake that willtravel in time over the horizontal stabilizer 86 and elevator surfaces94. The elevator surface has at least one trim tab 88 which can bealtered. The alterations of the trim tabs 88 are timed so as tocorrespond with and cancel out the noise and vibration generated by thepulsation of prop wash as those pulsation reach the elevator surface 94after being generated by the propeller 82.

[0046] Noise sensors 90 are mounted on the elevator and vibrationsensors 92 are mounted within the airplane 80 to measure theeffectiveness of the reduction efforts. The angular position of thepropeller 84 and the speed of the airplane are parameters that aremeasured independently of the generated noise and vibration. In thissystem, the plant is defined as the propeller 82, the propeller blades84, the hydraulic or mechanical line linkage 93 necessary to drive thetrim tabs 88 and the noise 90 and vibration 92 sensors. A controller 95receives the noise sensor signals 90 on connection 89 and the vibrationsensor signals 92 on connection 91 as well as propeller positions andairspeed inputs to control the instantaneous position of the trim tabs88.

[0047] Overview of the Invention

[0048] Referring now to FIG. 4, there is shown a block diagram of how acontrol system 100 is arranged in relation to the other portions of anactive, adaptive vibration and noise attenuation system (AAVNAS). Thecontrol system 100 is based on two neural networks: an emulator neuralnetwork (emulator) 102 and a controller neural network (controller) 104.Additional devices facilitate transferring inputs and outputs to andfrom the emulator 102 and controller 104 including: a reference signalgenerator (RSG) 106, time-based filter elements 108 and 109 for theinputs to the emulator and controller, a sensed noise and vibrationpreprocessing unit (SNVPU) 110, and a summing unit 112. Inputs to thecontrol system 100 include the sensed noised and vibration signalsflowing on connection 114 from one or more noise and vibration sensors115 as well as one or more parameters received from a parametersensor(s) 126 which sense the noise-independent parameter, e.g. angularposition of a rotor blade (as measured by the sine and cosine of theangle relative to some reference point on the rotor shaft) received onconnections 120, 121, and forward velocity of the vehicle received onconnection 119 (vehicle operation-condition input signal), which aremeasured independently of the noise and vibration and which are at leastpartially indicative (predictive) of the noise and vibration. Theoutputs of the parameter sensor(s) 126 are furnished on connections 119,120, and 121. The control system generates an output control signalflowing on a connection 122, which is used to drive a flap or otheraltering-device 124 so as to change the generated and thus sensed noiseand vibration received on connection 114 from the sensors 115, and isalso used as one of the inputs to the emulator time-based filter 108 forestimating the plant noise and vibration using the emulator.

[0049] See below for details on each step in the following processing.First the matrix coefficients of the neural net controller 104 areinitialized with small random values, and the matrix coefficients of theemulator 102 are initialized with the pre-computed values obtained fromthe neural net emulator training process to model the dynamics of theplant 130 (see FIG. 6). The initial state “k=0” (explained below, inthis paragraph) is initiated by the first sample clock of an A/Dconverter (described in connection with FIG. 6) and begins with thereference signal generation by the RSG component 106 on connection 150and the forward velocity signal (indicative of operating condition) onconnection 119 (in this context, “k” represents the current frame countof the system and is incremented by 1 for each subsequent A/D sampleclock cycle). The reference signal on connection 150 and the forwardvelocity signal 119 are placed into a time-based filter and 109 (forexample, digital tapped delay lines) at the input of the controller 104.During this initial state, the controller does a forward pass of thevalues stored in the time-based filter and 109 through the calculationsperformed on its neural network. This forward pass computes the outputcontrol signal on connection 122, which is connected to both thealtering-device 124 and the input time-based filter 108 of the emulator102. The output control signal is delivered to the altering-device 124and to the emulator time-based filter 108 during the next A/D sampleclock cycle.

[0050] The next state “k=1” is initiated by the next A/D sample clockand the process begins by first delivering the control signal computedduring the k-i state to the altering device 124 and to the time-basedfilter 108 of the emulator. New indicative parameters are received onconnections 119, 120, and 121 (e.g. vehicle forward velocity, sine andcosine of the blade angular position), and a new reference signal isreceived on connection 150. The signals on connections 119, 120, and 121are placed into the time-based filter 108 (e.g. the tapped delay lines)of the emulator 102, and signals on connections 119 and 150 are placedinto the time-based filter 109 of the controller 104. During this state,the emulator does a forward-pass of the values placed in the inputfilter 108 through the fixed matrix coefficients from the emulatortraining process as to constitute its neural network simulationcapability and produces an estimate of the noise and vibration signal onconnection 134 which is sent to the summing unit 112. During this state,the measured noise and vibration signals on connection 114 are alsoprocessed in the sensed noise and vibration preprocessing unit (SNVPU)110 and are delivered on connection 136 to the summing unit 112 asdigitized noise envelope and vibration signals.

[0051] The differences in the sensed and estimated noise and vibrationsignals 138 are calculated in the summing unit 112 and are used toassess, in real time, the emulator accuracy to the actual plant processand to determine whether emulator re-training is necessary (see belowfor options if re-training is necessary). The sensed digitized noiseenvelope and vibration signals on connection 136 from the SNVPU 110 arealso returned to the emulator 102. The emulator 102 then backpropagatesthese signals carried on connection 136 through its network in order tocalculate the gradient of the plant error signals with respect to theinput control signal received on connection 122. The plant errorgradient is then sent to the controller 104 on connection 140. Thecontroller uses a gradient-based algorithm (see the Controller sectionbelow) to backpropagate the plant error gradient through its neuralnetwork and adapt its matrix coefficients in layers 3, 2 and 1 (seeEmulator in Control Mode and Controller sections). The controller cannow do a forward pass of the filter tapped delay line reference values109 through its updated neural net matrix coefficients and calculate anew output control signal for the next state carried on the connection122. When the next A/D sample clock cycle occurs, “k” is incremented andthe described processing sequence in state “k=1” repeats itself witheach succeeding clock cycle.

[0052] If the emulator modeling of the actual plant process is notaccurate enough (based on the differences in the sensed and estimatednoise and vibration), retraining may be necessary (see Training theEmulator section below). The AANVAS may indicate this inaccuracy to theuser such devices as warning lights or computer printouts (on a monitor,printer, or any other type of computer output device). The user can thenimmediately halt the plant process and reinitiate the emulator trainingprocedure, or the user can note the need for retraining and defer theretraining procedure until a more convenient time.

[0053] Generalized Neural Network Description

[0054] Both the emulator 102 and the controller 104 have feed-forward,multi-layer architectures with input tapped delay lines 108 and 109 tocapture the temporal dynamic behavior of the plant required for controlof a dynamic system. In the preferred embodiment, the neural netprocessors (emulator 102 and controller 104) have three-layerarchitectures.

[0055] Referring now to FIG. 5, there is shown a generalizedthree-layer, feed-forward neural net processor. The neural net of FIG. 5has four inputs, (indicative/predictive) parameters h1, h2, and h3received on a connections 199, 200, and 201 (e.g. forward velocity ofthe vehicle, sine and cosine of the main rotor angular shaft position)and an input signal y received on a connection 202. The number ofindicative parameters is at least one and can vary according to theparticular application and should not be seen as a limitation on thepresent invention. Each indicative parameter assists the neural networkin building relationships useful to model and control the nonlinearplant dynamics at different vehicle operational points.

[0056] The nature of the input signal y received on connection 202depends upon whether the neural net of FIG. 5 is functioning as theemulator 102 or the controller 104. Also, the input signal y received onthe connection 202 to the emulator 102 depends upon whether the emulator102 is in its initial “plant model training” regime or is performing inits “adaptive control” regime during active control of the active,adaptive vibration and noise attenuation system (AAVNAS). The nature ofthese input signals, y, received on connection 202 and the “regimes”under which the neural net is being trained or used in adaptive controlmode are described in more detail below.

[0057] The input signal y received on connection 202 and input signalsh1, h2, and h3 received on connection 199, 200, and 201 are stored intime-based filter elements 108, preferably in the form of delay lines.These delay lines are similar to shift registers in which the currentinput signal is shifted in, each previously-stored input signal isshifted forward, and the input signal from N frames is shifted out whereN represents the number of available storage positions in the tappeddelay lines. The shifting is done at the beginning of each frame shortlyafter the start of the A/D clock cycle has occurred. These delay linesfilter the inputs to both the emulator 102 and controller 104 neuralnetworks and provide a time-based history of N signal values.

[0058] The generalized neural net of FIG. 5 preferably contains threelayers, with the first layer output vector x1=[X_(o) ⁽¹⁾, x₁ ⁽¹⁾, x₂ ⁽¹⁾. . . x_(Q) ⁽¹⁾]T (T denotes transpose), the second layer output vectorx2=[X_(o) ⁽²⁾, x₁ ⁽²⁾, x₂ ⁽²⁾ . . . X_(M) ⁽²⁾]^(T), and the third layeroutput vector x3=[x_(o) ⁽³⁾, x₁ ⁽³⁾, x₂ ⁽³⁾ . . . x_(L) ⁽³⁾]^(T). Eachlayer includes a matrix formed by the several row vectors in that layer.For example the matrix in the first layer is formed by the row vectorsw_(o) ⁽¹⁾ through w_(Q) ⁽¹⁾, where the dimension of each row vector is1×(N₁+N₂+N₃+N₄+1) resulting in a matrix with dimensions(Q+1)×(N₁+N₂+N₃+N₄+1). The input signal y(n) to the first layer isdelayed by N₁−1 samples and the forward velocity signal h1(n) and bladesine and cosine position signal h2(n) and h3(n) are delayed by N₂-1,N₃-1, and N₄-1 samples, respectively, (for example, in FIG. 5, N₂-1=2for a two-sample delay), and an input bias b1 is included to form atotal input vector of dimension (N₁+N₂+N₃+N₄+1)×1. This input vector ismultiplied with the matrix in the first layer (formed by row vectorsw_(o) ⁽¹⁾ through W_(Q) ⁽¹⁾) resulting in the intermediary output vectory1 with elements yo₀ ⁽¹⁾ through y_(Q) ⁽¹⁾. This vector is thenmultiplied element-by-element with a vector of sigmoidal functionsproducing the first layer output vector x1. The sigmoidal functionpreferably used in this neural net is defined by: $\begin{matrix}{{\sigma (y)} = \frac{1 - e^{- y}}{1 + e^{- y}}} & (1)\end{matrix}$

[0059] The same process is then repeated for the second layer in whichthe input vector is now defined by the output of the sigmoidal functionsfrom the first layer which are [x_(o) ⁽¹⁾, x₁ ⁽¹⁾, x₂ ⁽¹⁾ . . . x_(Q)⁽¹⁾] and a bias b2. This vector multiplies the matrix formed by rowsw_(o) ⁽²⁾ through w_(M) ⁽²⁾ in the second layer whose dimension is(M+1)×(Q+2) where M+1 is the number of nodes in the second layer and Q+2is the number of input channels into the second layer. The third layerperforms similar operations, and the output vector with elements [x_(o)⁽³⁾, x₁ ⁽³⁾, x₂ ⁽³⁾ . . . x_(L) ⁽³⁾].

[0060] In the emulator version of the generalized neural network, theoutput vector with elements [x_(o) ⁽³⁾, x₁ ⁽ 3), x_(o) ⁽³⁾ . . . x_(L)⁽³⁾] is summed with the digitized output sensor signals [sensor₀,sensor₁, . . . sensor_(L)] in summing unit 112 to produce outputs [e₀,e₁, . . .e_(L)], which constitute the outputs of the neural net.

[0061] In the preferred embodiment, the processing described above andshown in FIG. 5 is performed at 512 Hz, which is the sample rate of theneural net emulator and controller process. The sample rate is also thesame as the A/D clock rate. Each delay z⁻¹ is one sample interval.Therefore, in the preferred embodiment, the output of the third delay ofthe input signal y (stored in input filter element y(3)), is 3/512 of asecond later than the input signal y that gave rise to it.

[0062] Training the Emulator

[0063] Referring now to FIG. 6 there is shown an arrangement for theemulator 102 in its training mode to learn the plant 130. In using thepresent invention to control an AAVNAS, the first operation is to trainthe emulator 102 to model the plant nonlinear dynamics. Methods of plantidentification for linear systems are well known in the art. One exampleof such methods is described in U.S. Pat. No. 4,677,676, issued toEriksson on Jun. 30, 1987 ('676 patent). To model a linear system atspecific frequencies, only one amplitude/phase sinusoid input isrequired per frequency since with different amplitudes, the output islinearly related. However, to model nonlinear systems, the presentinvention generates various random samples of amplitude/phase perfrequency to determine adequately the nonlinear input/output mapping ateach frequency.

[0064] Training a neural net is a process that begins with a synthesizedprobe signal. In the preferred embodiment, a multi-harmonicmulti-pattern signal is used as the probe signal and is composed ofuncorrelated patterns. Each pattern is generated by the superposition ofone or more harmonics, with the amplitudes and phases generated byrandom number generators; and the pattern is repeated for a number ofcycles to provide adequate duration of plant excitation. In thepreferred embodiment, the number of harmonics is seven (1P-7P). Eachpattern in the probe signal is generated by re-running the random numbergenerators to provide an alternate set of fourteen random amplitudes andphases. The number of patterns required for training the neural net isbased on how well the emulator can generalize with patterns not usedduring training. If the generalization is poor, the number of patternsis increased until the neural net achieves adequate generalization. Thisprobe signal is constructed in the controller 104 and is delivered onconnection 222. The excitation signal on connection 222 is deliveredthrough a D/A converter 224 to control the mechanism of the alteringdevice 124 to the physical portions of the system to be controlled.

[0065] During the training process, the system under control is operatedin its customary environment, and the outputs of the plant noise andvibration sensors 115 are gathered. The plant reacts to the changes inthe altering-device 124 created by the probe excitation signal on line222. In the helicopter example described above and shown in FIG. 1, thehelicopter would be operated at several predefined flight conditions fora specified flight regime while the flaps 44 are actuated by theexcitation signal (described above), and the noise and vibration sensorsrecord the changes that occur due to the flap actuations.

[0066] The sensed and gathered (processed and summed) noise andvibration signals are then related to the input excitation signal onconnection 222 driving the altering-device 124, and to the indicativeparameters on connections 119, 120, and 121. This data-collectionprocess is done during normal operation of the system. In oneembodiment, the collected data are stored by means of a mass computerstorage device such as a magnetic tape or disk drive. The data are thenanalyzed off-line to build the relationship between input and outputdata. Alternatively, the data need not be collected and then processedoff-line but can be processed through the neural net in real time,thereby obviating voluminous data collection. The preferred embodimentof the present invention uses the real time neural net emulatorprocessing to build the input/output data relationship.

[0067]FIG. 6 assumes real-time processing of the sensed noise andvibration signals on connection 114 from the changes in thealtering-device 124 driven with the multi-harmonic multi-patternexcitation signal on connection 222 generated in the controller 104. Thesensed noise and vibrations on connection 114 are delivered to the SNVPU110 which processes the sensed noise and vibration into digitized noiseenvelopes and vibration signals. Those signals are transferred on aconnection 136 to a plurality of summing circuits 112. This processingby the SNVPU 110 is described in greater detail below.

[0068] In the preferred embodiment of the present invention, theprocessing in the emulator 102 and controller 104 (described below) isperformed at 512 Hz. The probe signal on connection 122 and indicative(predictive) parameter signals on connections 119, 120, 121 are alsosampled at 512 Hz. These digital signals appear at the input time-basedfilters 108 to the emulator 102. The emulator 102 of FIG. 6 is in theform of the neural net circuit of FIG. 5. The probe signal on connection122 and indicative (predictive) parameter signals on connections 119,120, and 121 of FIG. 6 correspond to the input signal y on connection202 and the parameter signals h1, h2, and h3 on connections 199, 200 and201, respectively, of FIG. 5. The summing circuits 0-L of FIG. 5correspond to the summing circuit 112 of FIG. 6. The emulator 102estimated noise and vibration outputs transmitted on connection 134 ofFIG. 6 corresponds to the output signals [x_(o) ⁽³⁾, x₁ ⁽³⁾, x₂ ⁽³⁾ . .. x_(L) ⁽³⁾] from the third layer of the neural net of FIG. 5 and isdelivered as a negative input to the summing circuit 112. Therefore, thedifference between the measured noise and vibration signals [sensor₀,sensor₁, . . . sensor_(L)] 136 and the emulator 102 estimated noise andvibration outputs 134 are the error signals that are delivered as theadjustment input to the emulator 102 on connection 138 to adjust themany matrix multiplication coefficients of the layers of nodes of theneural net circuit of FIG. 5. As stated above, the number of indicativeparameter signals could be one or more depending upon the specificapplication in which the AANVAS is used.

[0069] The ways that error signals are used to adjust the multiplicationcoefficients of a neural net are well known and available from severaltexts on neural net circuits. Two textbook examples are:

[0070] (1) Neural Networks—A Comprehensive Foundation by Simon Haykin,©1994, Macmillan Publishing Company, ISBN: 0-02-352761-7; and

[0071] (2) Neuro-Control and its Applications by Sigeru Omatu, MarzukiKhalid, and Rubiyah Yusof, ©1996, Springer-Verlag, ISBN: 3-540-19965-9.

[0072] The Haykin reference provides a description of neural netarchitectures and training. The Omatu, et al. reference describesapplications of neural nets used in industrial control systems.

[0073] To train or teach the emulator 102, the error outputs (in theform of difference signals) from the summing circuits 112 of FIG. 6 areused with a backpropagation algorithm or with nonlinear optimizationalgorithms such as quasi-Newton, conjugate gradient or Kalman filteringtechniques to adjust the multiplier coefficients of the emulator 102until the emulator produces an estimate signal within a arbitrary errortolerance E(l) to the output of the SNVPU 10, from the sensed noise andvibration received on connection 114. When that happens, the emulator102 is said to have “learned” how to emulate the sensed noise andvibration received on connection 136. In this way, the emulator 102learns to model the intricate nonlinear and time-varying dynamics of theplant 130 from the input probe signal to the changed altering-devicestates through to the noise envelope and vibration signals.

[0074] The Indicative Parameters

[0075] Selection of the input parameters is important to the efficiencyof the system in modeling and controlling noise and vibration. Eachindicative parameter should be a value that is related to the plantnoise and vibration which is being controlled at the various operationalpoints of the vehicle.

[0076] One advantage of the present invention is its ability to usedifferent indicative parameters to allow the AAVNAS to reduce noise andvibration over a variety of operating conditions using the same neuralnetwork topology. For example, in the helicopter application describedabove, the forward velocity of the helicopter is one candidateindicative parameter. The system can operate without the velocity inputsignal at specified discrete operational points. However, at thein-between operational points, that is during transition from oneoperational point to the next, the system would not perform as wellsince the plant noise and vibration change from one operational point tothe next, and the emulators are trained to predict noise and vibrationonly at the specified operational points.

[0077] In the helicopter example, assume the following operationalpoints: BVI noise at 80 knots, vibration at 120 knots, and HSI noise at160 knots. These are three operational points, so we would have to trainthree emulators to operate at the specified points. However, the systemwould not function as well during the transition from one operationalpoint to the next since the emulators are only applicable at the trainedpoints. To provide continuous operation of the system from 80 knots-160knots, we may have to train eight (or more) emulators to provide plantnoise/vibration emulation at eight operational points within the flightregime of 80-160 knots. It should be realized that the actual number ofplant emulators required to be trained for a specified flight regimewill be dependent on how much the plant noise and vibration dynamicsvary as a function of forward velocity and this must be determined fromdata analysis. In this example, one solution would be to have theemulators downloaded and switched at ten knot increments (based on someinput velocity signal) and some form of emulator interpolation wouldstill have to be done for the in-between points. This approach may work,however instead of storing in memory eight different emulators (for theprevious example), downloading emulators and interpolating forin-between points, one alternative and better solution is to train oneemulator for all eight operational points, by using an input velocitysignal during the training process. Once trained at the eightoperational points, the neural net emulator should be able to retrievethe appropriate plant emulation based on the input velocity signal. Inaddition, for the in-between points, the neural net emulator willautomatically provide the necessary interpolation. So, with thisapproach, there is no need to store (eight or more) emulators in memory,download the appropriate emulator or do the interpolation.

[0078] For vehicles that operate at one operational point in the flightregime, there is no need for a velocity input signal. For vehicles thathave to operate over a flight regime at multiple operational points, foroptimum results, the control system should run continuously. Therobust/efficient approach is to use a velocity input signal and maybeother inputs as well like payload, to provide the proper plant emulationat the multiple operational points and automatic interpolation for thein-between points during the continuous operation of the system. Neuralnets do this interpolation very well.

[0079] Preprocessing the Noise and Vibration Input Signals

[0080] Referring now to FIG. 7, there is shown a block diagram of thesensed noise and vibration preprocessing unit (SNVPU) 110. The basic rawform of the sensed noise and vibration signals is a large collection ofvalues representing the intensity of the noise or vibration. In order toevaluate the noise and vibrations being produced, the noise andvibration must be reduced into digital quantitative values which arethen evaluated with an objective function. The SNVPU 110 performs thisreduction by isolating the noise or vibration sensed by sensors 115 atparticular target frequencies and quantifying the value of the signalbeing sensed at each of those target frequencies.

[0081] The SNVPU 110 receives sensed noise and vibration signals inanalog form on connection 114 from the sensors 115. In the preferredembodiment, the analog sensor signals on connection 114 are firstdigitized in the A/D converter 231 and bandpass filtered in the signalprocessor 232 in order to extract (i) the frequency bandwidth of thenoise signature and (ii) the vibratory loads at the vibrationfrequencies desired to be controlled. Bandpass filter design is wellknown. One method of creating this filter 232 is by using a softwaretool called Matlab produced by a company called The MathWorks, Inc., 24Prime Park Way, Natick, Mass. 01760-1500 (508) 647-7000. In thepreferred embodiment, this filter is a digital infinite impulse response(IIR) implemented in software on a DSP (digital signal processor).

[0082] The digital vibration signals are sampled at the execution rateof the controller and emulator (in the preferred embodiment this rate is512 Hz) and are bandpass filtered in signal processor 232 as previouslydescribed to generate the desired vibratory loads. The bandpass filteredvibration signals obtained from multiple cabin locations are thentransmitted to the summing circuit 112 on connection 136.

[0083] The digital noise signals to be controlled are high1y variableand are sampled at a rate higher than the execution rate of thecontroller and emulator. In the preferred embodiment, this digital noisesignal sample rate is 2560 Hz. To reduce the DSP processingcomputational requirement, the noise signals are transformed to basebandfrequencies using a Hilbert Transform digital envelope detector. Thecontroller is then used to reduce the BVI noise envelope signals ratherthan the high1y variable noise signals, thus reducing the overall DSPcomputational requirement. To generate the noise envelope signals, thedigital noise signals are first bandpass filtered in signal processor232 to pass only the BVI frequency band signature. The signals that exitthe bandpass filter are then analyzed in a Hilbert Transform envelopedetector filter 234 to identify the envelope of the noise signal loci.Noise sensor signals at multiple locations on the blade are processedthrough the envelope detector, and the resulting noise envelope signalsare then transmitted on the connection 136 to the summing circuit 112.

[0084] Identifying a signal envelope is conceptually like detecting anAM radio signal, but with digital data signals. The block diagram inFIG. 8 describes the Hilbert Transform envelope detector. The upper pathgenerates the Hilbert Transform of the input noise signal (.i.e., xhat)by filtering the input noise through a FIR (Finite Impulse Response)Hilbert Transform digital filter. The textbook “Digital SignalProcessing by Alan Oppenheim and Ronald Schafer, ®1975, Prentice-HallInc., ISBN: 0-13-214635-5” describes one method to derive the weights ofthe FIR Hilbert Transform filter. The FIR Hilbert Transformer adds adelay of (K-1)/2 taps (K is the FIR filter length). The lower path ofthe block diagram compensates for this delay by introducing a pure delayof (K−1)/2 to the input noise signal. The noise envelope signal is thengenerated by taking the square root of the noise power obtained bysumming the power of the Hilbert transformed signal and the originaldelayed signal (i.e., sqrt(x^ 2+xhat^ 2)). The envelope of the noisesignal on connection 135 is then lowpass filtered and decimated, sincethe baseband signal envelope in the preferred embodiment has a narrowfrequency bandwidth in relation to the sample rate of the A/D converter,and delivered on connection 136 to the summing circuit 112. In thepreferred embodiment the A/D sample rate for the input noise signals isat 2560 Hz and the processed noise envelope signals are decimated orreduced in numbers by a factor of five down to the 512 Hz sample rateprocessing of the neural net emulator and controller.

[0085] In the preferred embodiment, there are a plurality of noise andvibration sensors. For each of the sensors, there is one or more of thecombination of IIR and FIR filters which isolate the noise or vibrationbeing transmitted at the desired frequency bands and quantify the valueof the signal being transmitted. The quantified value is then analyzedagainst the estimated noise and vibration values from the emulator 102to determine the error in the plant model.

[0086] Once the sensed noise and vibration signal has been quantified,that quantified value is numerically combined with quantified valuesfrom the other signals in an objective function.

[0087] This objective function is the quantified summation of the actualsensed noise envelope and vibration signals desired to be reduced orsuppressed. The objective function represents a “strength” measurementof the objectionable noise and vibration and, as such, is one possiblecriterion by which the efficacy of the control efforts is judged. Oneskilled in the art could easily derive other criteria related to thereduction of noise and vibration by which to judge the efficacy of thecontrol efforts. By reducing the resultant value of the objectivefunction, the AAVNAS has necessarily reduced the objectionable noise andvibration. In the emulator training mode, the emulator is trained togenerate an output that is an estimated value representing the resultsof the vibration and noise envelope signals. In the control mode, thecontroller 104 is adapted to produce control signals to thealtering-device to minimize the objective function value calculated fromthe noise and vibration signals that occur after the altering-device hasbeen changed via the control signals. In the preferred embodiment, withfive vibration sensors and two noise sensors, that objective functionhas the exemplary form of:

Objective Function=J==a1*[(k1*Fx ²)+(k2*Fy ²)+(k3*Fz ²)+(k4*Mx ²)+(k5*My²)]+a2*[(k6*P12)+(k7*P22)]=a1*(vibration_objective_function)+a2(noise_objective_function)  (2)

[0088] Fx, Fy, Fz, Mx, and My are the digital vibration measurements atthe desired frequency (i.e., 4P for helicopter fixed frame hub loads).One example of vibration measurements comprises the readings fromaccelerometers mounted in the cabin of a helicopter measuring the cabinforces and moments.

[0089] P1 and P2 are noise envelope measurements. One example of noisemeasurements comprises the readings from pressure transducers mounted onthe rotor blade of a helicopter.

[0090] k1-k7, and a1-a2 are weighting coefficients which allow theactive, adaptive vibration and noise attenuation system (AAVNAS) toemphasize certain components more than others (i.e., k1-k7) and alsoemphasize more the vibration or noise objective function (a1, a2).

[0091] The objective function describes what the controller is toreduce. For example, with a1=a2=0.5, the AAVNAS equally reduces thepower of the vibration signal and the power of the noise envelope.Different weights can be used to obtain a trade-off between noise andvibration reduction.

[0092] Emulator in Control Mode

[0093] It has been suggested in the reference by Nguyen D. and Widrow B.{“The truck backer-upper: an example of self-learning in neuralnetworks”, (1989), Proc. Int. Joint Conf. On Neural Networks, WashingtonD.C., Vol. 2} that a neural network could be used to emulate a nonlinearplant, and the trained emulator could therefore be used as a channel forthe backpropagation of errors to provide the plant gradient informationto allow adaptation of the neural net controller matrix coefficients viaa gradient descent algorithm. This approach is also used herein toprovide adaptation to the neural net controller. Referring now to FIG.9, there is shown a block diagram of the emulator 102 during the controlmode (after the plant model training has occurred) in the preferredthree-layer embodiment. In the control mode, the emulator 102 is notadaptive; and the emulator weighting coefficients stored in the rowvectors 300 are fixed.

[0094] During the emulator forward pass, the output control signal u(n)received on connection 122 corresponds to the y input signal received onconnection 202 in FIG. 5; and the (indicative/predictive) parametersignals (e.g. the forward velocity and the sine and cosine of the rotorshaft angular position) received on connections 119, 120, and 121correspond to the h1, h2 and h3 references received on connection 199,200, and 201 in FIG. 5. These inputs go through tapped delay lines 108.The content of the delay lines 108 is stored as the vectorx_(in)(=[u(n), u(n-1), . . . u(n-Nu1+1), h1(n), h1(n−1), . . .h1(n-N2+1), h2(n), h2(n−1), . . . h2(n-N3+1), h3(n), h3(n−1), . . .h3(n-N4+1), b1]^(T)) in memory location 280, as described in connectionwith the u(n), h1(n), h2(n) and h3(n) delayed signals in FIG. 9. In thepreferred embodiment, the input vector contains all of the delayedvalues stored in the time-based filter 108. However, an alternateembodiment would be to compose the input vector from selected valuesstored in the time-based filter 108. The input vector is processed in aforward direction (“forward pass”) through the fixed row vectors 300 ofthe emulator 102 producing the sigmoidal function output vectors on alayer-by-layer basis at the first layer x1 stored in memory location310, second layer x2 stored in memory location 312, and third layer x3stored in memory location 314. At each layer, these outputs are storedin memory locations 310 and 312 and 314 and are used during thebackpropagation of sensor signals [sensor₀, sensor₁, sensor₂ . . .sensor_(L)] received on connection 136 to generate the gradient of theinstantaneous objective function with respect to the input controlsignal which is passed to the controller on connection 140. Thefollowing equations summarize the backpropagation of the error signalsused in the present invention to provide gradient information to theneural net controller. Defining the summed squared errors at the systemoutput as the objective function to be minimized with the neural netcontroller by: $\begin{matrix}{J = {{\sum\limits_{l = 0}^{L}\quad \left\lbrack \left( {\sqrt{c_{l}} \cdot {d_{l}(n)}} \right)^{2} \right\rbrack} = {\sum\limits_{l = 0}^{L}\quad {e_{l}(n)}^{2}}}} & (3)\end{matrix}$

[0095] c_(l) are the objective function weights (for the exemplary formin equation (2), (c1=a1*k1, c2=a1*k2, . . . c5=a1*k5,c6=a2*k6,c7=a2*k7), d_(l) (n) are the vibration and noise sensor signals [Fx, Fy,Fz, Mx, My, PI, P2], e_(l) are the error signals to be minimized by thecontroller and l=0,1, . . . L (L=7 in equation (2)). The gradient of theobjective function with respect to the ‘j'th tap’input control signalu(n−j) (where j=0, 1, . . . Nu−1) can be shown to be: $\begin{matrix}{\frac{\partial J}{\partial{u\left( {n - j} \right)}} = {\sum\limits_{q = 0}^{Q}\quad {w_{j,q}^{(1)}{\delta_{q}^{(1)}(n)}}}} & (4)\end{matrix}$

[0096] The weight w_(j,q) ⁽¹⁾ is the ‘j−q’ fixed coefficient thatrelates input j to output neuron (a neuron is a node or element withinthe network) q in the first layer of the emulator, and the correspondinggradients at the q'th node in the first layer is: $\begin{matrix}{{\delta_{q}^{(1)}(n)} = {\frac{\partial J}{\partial{y_{q}^{(1)}(n)}} = {{\sigma \left( {y_{q}^{(1)}(n)} \right)} \cdot {\sum\limits_{m = 0}^{M}\quad {w_{q,m}^{(2)}{\delta_{m}^{(2)}(n)}}}}}} & (5)\end{matrix}$

[0097] Similarly the gradients at the m'th node in second layer and atthe l'th node in the third layer of the emulator are: $\begin{matrix}{{\delta_{m}^{(2)}(n)} = {\frac{\partial J}{\partial{y_{m}^{(2)}(n)}} = {{\sigma^{\prime}\left( {y_{m}^{(2)}(n)} \right)} \cdot {\sum\limits_{l = 0}^{L}\quad {w_{m,l}^{(3)}{\delta_{l}^{(3)}(n)}}}}}} & (6) \\{{{\delta_{l}^{(3)}(n)} = {\frac{\partial J}{\partial{y_{l}^{(3)}(n)}} = {{2 \cdot {\sigma^{\prime}\left( {y_{l}^{3}(n)} \right)} \cdot {e_{l}(n)}}\quad = {2 \cdot {\sigma^{\prime}\left( {y_{l}^{3}(n)} \right)} \cdot \sqrt{c_{l}} \cdot {d_{l}(n)}}}}};{{d_{l}(n)} = {{sensor}_{l}(n)}}} & (7)\end{matrix}$

[0098] The weight w_(q,m) ⁽²⁾ is the ‘q−m’ coefficient that relates theq'th input from the first layer to the m'th neuron in the second layer,and w_(m,l) ⁽³⁾ is the ‘m−l’ coefficient that relates the m'th inputfrom the second layer to the l'th neuron in the third layer. There are(Q+1)×(Nu1+N2+N3+N4+1) coefficients in the first layer, (M+1)×(Q+2)coefficients in the second layer and (L+1)×(M+2) in the third layerincluding the bias weights. The derivative of the activation functionis:

σ((y _(k) ^((i))(n))=1−x _(k) ^((i))(n)·x _(k) ^((j))(n)  (8)

[0099] where x_(k) ^((i)) is the output of the activation function atthe i'th layer (i—1,2,3) for node k with Q+1 nodes in the first layer,M+1 nodes in the second layer and L+1 nodes in the third layer. Thesesignals x_(k) ^((i)) are computed and stored in column vectors x1, x2and x3 at location 310, 312 and 314 during the forward pass through theemulator, and are used during the reverse pass to compute the reversepass gradients. The reverse pass starts with the backpropagation of thesensor signals on connection 136 in the third layer to compute thegradient at the output of the emulator using equation (7), this allowsthe gradient computation to be determined at the second layer usingequation (6), at the first layer using equation (5) and then equation(4) determines the instantaneous plant gradient with respect to the j'thinput control signal in the delay line. This gradient is then deliveredon connection 140 to the controller 104 to allow the adaptation of theneural net controller matrix coefficients using a temporalbackpropagation gradient algorithm (see Controller section, below).

[0100] Furthermore, the estimated noise envelopes and vibration signalsstored in memory location 314 are negated and added to the digitizedsensed noise envelopes and vibration signals [sensor₀, sensor₁, sensor₂. . . sensor_(L)] on connection 136 to verify that the emulator (fixedcoefficients) produces an estimate signal within an arbitrary errortolerance E(l) on connection 138, thus making it valid to use the datastored in memory locations 310, 312 and 314 for backpropagation of theerror sensors to generate the plant gradient using equation (4).

[0101] The Reference Signal

[0102]FIG. 10 illustrates how the angular rotor position is used tocreate the input reference signal to the neural net controller. Thereference signal generator 106 uses the angular position of the rotor tocreate sinusoids of harnonics of rotor frequency P. In the preferredembodiment, the angular position of the rotor is transmitted from ameasuring device 240, (e.g., a sensor connected to the rotor on ahelicopter to measure the rotor position) on connection 242. One exampleof such a sensor is the cam with one or more lobes with an associatedcam-following switch such as has been used in the past on distributorsfor automobiles. Another example of such a sensor is a photoelectricdisk with an associated light sensor. The disk has differing reflectiveproperties such as white stripes extending radially from the center. Thelight sensor detects the differences in the photoelectric disk and usesthese differences to calculate an angular position. Another example ofsuch a sensor is a starwheel with an associated magnetic sensor, as usedin the distributors of new automobiles. The starwheel has one or moreteeth along the outside edge. As the starwheel rotates, the teeth passclose to one or more fixed magnetic sensors which detect a change in amagnetic field. This change can then be used to calculate angularposition.

[0103] In the preferred embodiment, the reference signal transmitted onconnection 150 is a time series sinusoidal signal based on a combinationof one or more harmonics of P, with P being measured for the soundproducing system. When summing the various harmonics, weighting factorsmay be included to give a different weight to the various harmonics inthe resulting output wave. In the preferred embodiment, the referencesignal is a time-sampled combination of the 2P and 3P harmonics (for thehelicopter reference) as represented by the following equation:

referenceSigSine=(TWOPWEIGHT*sin(2*rotorposition(n))+(THREEPWEIGHT*sin(3*rotorposition(n))

[0104] In the preferred embodiment, the TWOPWEIGHT factor is 0.8 and theTHREEPWEIGHT factor is 0.2. The reference signal generator (RSG) unit106 in FIG. 10 illustrates the reference waveform that would be createdby summing a 2P and 3P harmonic wave. Reference signals for otherembodiments would be generated in a similar fashion.

[0105] The Controller

[0106] Referring now to FIG. 11, there is shown a block diagram of thecontroller 104 processor during the control mode (after the plant modeltraining of the emulator 102 has occurred) in the preferred three-layerembodiment. In reference to FIG. 5, the inputs to the controllercomprise the input signal y(n), received on connection 202 and h2(n)received on connection 199, respectively, as signals v1(n) and hv2(n),which are then filtered through a time-based delay line filter and 109to result in the input vector v(n)(=[v1(n),v1(n−1), . . . v1(n−Nr1+l),hv2(n),hv2(n−1), . . . hv2(n-Nr2+1), b1]^(T) ) (b1 is the input bias asdescribed in FIG. 5 and the associated description of FIG. 5). For thecontroller, the vl (n) values received on connection 202 are thereference signals generated by the reference signal generator 106described in connection with FIG. 10 which, in the preferred embodimentof the present invention, comprises a combination of sinusoids of a basereference rate delivered on connection 150.

[0107] In the preferred embodiment, the hv2(n) signal received onconnection 199 is the forward velocity of the vehicle indicative of theoperating condition and delivered on connection 119. However, in onealternate embodiment of the present invention, the hv2(n) vector may benull. Whether the hv2(n) vector is null or populated depends on whichindicative parameters are selected for the particular embodiment of theAAVNAS.

[0108] Connections 150 and 119 describing the input to the controller104 are the same connections with connection 202 and 199 describing theinput to the generic neural network. In the preferred embodiment, theinput vector v(n) contains all of the delayed values stored in thetime-based filter 109. However, an alternate embodiment would be tocompose the input vector from selected values stored in the time-basedfilter and 109.

[0109] The controller 104 does a forward pass of the time-based filterreference vector v(n) through its neural net matrix coefficients togenerate the output control signal u(n) on connection 122 which iscomposed of a combination of phase and amplitude shifted harmonics ofthe base reference rate P. Even though the reference signal containspossibly 2P and 3P frequencies, the nonlinear sigmoidal functions in theneural net controller can model higher order harmonics that may bepresent in the error signals. In the preferred embodiment, the neuralnet generates a control signal that contains a combination of the 2P,3P, 4P, and 5P harmonics for the reduction of vibration and noise inreference to the helicopter application determined from digital closedloop simulations of the neural network and math models of the blade-flapBVI noise dynamics.

[0110] The preferred embodiment of the controller has three layers withthe third layer comprising a single element (neuron) because thecontroller only transmits a single control signal on connection 122 tobe used by the altering-device 124 (see Use of the Control Signalsection, below) and also to be used as an input into the time-basedfilter 108 of the emulator 102. While data are progressing in a forwarddirection through the several layers of the controller 104 (forward passof the reference signal), the intermediate results from the input, firstand second layer are stored in memory locations to be used during thebackpropagation of the gradients calculated by the emulator 102 thatoccurs during the reverse pass.

[0111] In FIG. 11, the input reference column vector and delayed copies{v(n),v(n-1), . . . v(n−j), .} are stored in memory location 315, thefirst layer output column vector and delayed copies {x1c(n),x1c(n-1), .. . x1c(n−j), . . . } are stored in memory location 320 and the secondcolumn vector and delayed copies {x2c(n),x2c(n-1), . . . x2c(n−j), . . .} are stored in memory location 322 respectively (Note: j=0, 1, . . .Nu1-1, and Nu1 is equal to the number of taps in the emulator inputdelay line 108 filtering the control signal u(n)). The output signal onconnection 122 represents the results stored in location 324 and drivesan altering-device 124 (see also FIG. 4 and the associated description)to minimize the fluid flow disruptions which result in the sensed noiseand vibration.

[0112] Additionally, the output control signal on connection 122 is aninput to the time-based filter 108 of the emulator 102, which is used inthe emulator's plant model for estimating noise and vibration and forcomputing the gradient of the objective function with respect to theplant input. After calculating and sending the output control signal onconnection 122, the controller 104 waits for the emulator 102 to returnthe instantaneous gradient of the objective function on connection 140.

[0113] During the reverse pass, the preprocessed sensed noise andvibration signals from unit 110 (SNVPU) are received by the emulator onconnection 136 (FIG. 9) and are then backpropagated through the emulator102 to generate the instantaneous gradient of the objective functionwith respect to the j'th control input signal u(n−j) (equation (4)).This gradient is received by the controller and is then backpropagatedthrough the controller network using a temporal backpropagationalgorithm for FIR neural networks (“Temporal Backpropagation for FIRNeural Networks”, Wan E.A., 1190, IEEE International Joint Conference onNeural Networks, San Diego Calif.) since the controller and emulator areseparated by the tapped delay line.

[0114] Using equation (4), the coefficients of the third layer in thecontroller is now adapted using a gradient descent algorithm:$\begin{matrix}\begin{matrix}{{w^{({3c})}\left( {n + 1} \right)} = {{w^{({3c})}(n)} - {\mu {\sum\limits_{j = 0}^{{{Nu}\quad 1} - 1}\quad {\frac{\partial J}{\partial{u\left( {n - j} \right)}}\frac{\partial{u\left( {n - j} \right)}}{\partial{y^{({3c})}\left( {n - j} \right)}}\frac{\partial{y^{({3c})}\left( {n - j} \right)}}{\partial w^{({3c})}}}}}}} \\{= {{w^{({3c})}(n)} - {\mu {\sum\limits_{j = 0}^{{{Nu}\quad 1} - 1}{{\delta^{({3c})}\left( {n - j} \right)} \cdot {{x2c}^{T}\left( {n - j} \right)}}}}}}\end{matrix} & (9)\end{matrix}$

[0115] Referring to FIG. 11 and equation (9), W^((3c)) is the row vectorin the third layer of the neural net controller, j is the time delayindex (j=0, 1, . . .Nu1-1), in the input delay line of the emulatorwhich filters the input control signal u(n), u is the convergenceparameter of the gradient algorithm and can be made adaptive asspecified in the Haykin reference via the delta-bar-delta learning rule,and $\begin{matrix}{{\frac{\partial J}{\partial{y^{({3c})}\left( {n - j} \right)}} \equiv {\delta^{({3c})}\left( {n - j} \right)}} = {{\sigma^{\prime}\left( {y^{({3c})}\left( {n - j} \right)} \right)} \cdot \frac{\partial J}{\partial{u\left( {n - j} \right)}}}} & (10)\end{matrix}$

[0116] was previously determined from the emulator backpropagation. Theδ^((3c))(n−j) gradient is now backpropagated through the neural netcontroller to determine the corresponding gradients in the second layerδ_(p) ^(2c))(n−j) and first layer δ^((1c))(n−j), (p=0, 1, . . . P,k=0,1, . . . K,j=0,1, . . . Nu1-1) and provide the adaptation of thecontroller matrix coefficients. The p'th and k'th row vectors in thesecond and third controller layer respectively are then updated usingthe following update rules: $\begin{matrix}{{w_{p}^{({2c})}\left( {n + 1} \right)} = {{w_{p}^{({2c})}(n)} - {\mu {\sum\limits_{j = 0}^{{{Nu}\quad 1} - 1}\quad {{{\delta_{p}^{({2c})}\left( {n - j} \right)} \cdot x}\quad 1{c^{T}\left( {n - j} \right)}}}}}} & (11) \\{{w_{k}^{({1c})}\left( {n + 1} \right)} = {{w_{k}^{({1c})}(n)} - {\mu {\sum\limits_{j = 0}^{{{Nu}\quad 1} - 1}\quad {{\delta_{k}^{({1c})}\left( {n - j} \right)} \cdot {v^{T}\left( {n - j} \right)}}}}}} & (12)\end{matrix}$

[0117] p=0,1, . . . P (nodes in second layer)

[0118] k=0,1, . . . K (nodes in first layer)

[0119] The input and first layer column vectors and delayed copies,i.e., {v(n),v(n-1), . . . v(n−j), . . . , x1c(n), x1c(n-1), . . .x1c(n−j), . . . } previously stored in memory locations 315/320, and{x2c(n), x2c(n-1), . . . ,x2c(n−j) . . . } from the second layer storedin memory location 322, are now used in the update rules of equations9-12. In addition, the gradients at the current cycle and at Nu-1previous cycles of the first, second and third layer {δ^((3c))(n−j),δ_(p) ^((2c))(n−j), δ_(k) ^((1c))(n−j), where p=0, 1,. .P′ and k=0, 1, .. . K, j=0, 1 . . . Nu1-1} are also maintained in memory since thesecomponents are required in the neural net matrix coefficients updaterules. The controller matrix coefficients are then modified in real timeaccording to equations 9-12.

[0120] After the controller matrix coefficients have been updated, thecontroller does a forward pass of the time-based filter reference vectorv(n) through its updated neural net matrix coefficients and calculates anew output control signal on connection 122. The control signal drivesthe altering device 124 to reduce the noise and vibration and alsodrives the emulator 102 to compute a new plant gradient for the nextstate. A complete elimination of the noise and vibration is not alwaysachievable. The controller adapts its matrix coefficients during eachstate to determine the optimum control solution that will drive theplant to generate the minimal noise possible under the current operatingconditions.

[0121] The initial weights of the controller can be set to optimumvalues which can be either pre-computed from an off-line digitalsimulator, or computed and stored in computer memory-based on a previousoperation of the vehicle. In the off-line pre-computed scheme, thedigital simulator consists of the neural net controller configured toreduce the output of the neural net emulator which models the plantnoise and vibration output signals. In the simulation, the neural netcontroller adapts its coefficients to reduce the emulator equivalentplant noise and vibration outputs. The converged set of controllermatrix coefficients from the simulation can then be used to initializethe real time controller. In the absence of an off-line simulator, andfor a first-time operation, the real time controller matrix coefficientsare set to small random values, and the controller training at theoperational points in the specified operation regime takes place in realtime. Empirically, the initial values have been determined to affect thecontroller's performance relative to the function of produced noise andvibration. Initial non-random values can result in the controllergetting stuck at local minima in the produced noise function, thushalting the move toward the global solution. Random initial values aremore likely to avoid the pitfall of local minima in the vibration andnoise function.

[0122] In the preferred, helicopter embodiment, once the firsthelicopter flight is complete, the controller has been trained andtested at various operational points for the specified flight regime. Acontrol solution for each operational point in the flight regime is nowstored in the computer memory and can be accessed to initialize thecontroller for the next flight operation using the forward velocity asthe indicator in selecting the appropriate flap control solution.Therefore, during the next flight operation, the controller willinitialize itself in real time with the closest optimum solutioncomputed in the previous flight, using the current flight conditioninput signal. This approach is realizable since neural nets have aninherent capability of interpolation; and even though the operationalpoint may be slightly different from the previous flight, the neural netcontroller will interpolate to provide the best starting point solution.Since this starting solution is close to the optimum, the controllerwill quickly converge to the optimum solution.

[0123] The number of neurons in each layer is dependent on externalfactors. For the controller, the last (third in the preferred embodimentof the present invention) layer comprises a single neuron because onlyone control output is produced by the controller. The number of neuronsin the first, second and any subsequent intermediate layers represents atradeoff between two competing factors. The first factor iscomputational resources, i.e. the amount of computer time available toexecute the algorithm. Each neuron added increases the amount ofcalculation necessary to generate a control signal. The second factor iscontroller performance in noise and vibration reduction. More neuronsincreases the ability of the controller to generate control signalsgiving better noise and vibration reduction. After a certain number ofneurons (which depends on the complexity of the plant being modeled),additional neurons result in only marginal performance improvements.Available computational resources are balanced against the marginalperformance improvements when determining whether to add another neuronto the neural net first and second layers.

[0124] Use of the Control Signal

[0125] Referring now to FIG. 12, there is shown a system for using onecontrol signal to control multiple altering-devices. In applicationssuch as the helicopter rotor and ship propellers discussed above,multiple surfaces exist over which the fluid is flowing and producingnoise and vibration. In some of these applications, the surfaces (rotorblades or propeller blades) are all following the same path andencountering the same conditions. A separate controller for each bladewould be unnecessarily redundant. Instead, a control signal onconnection 122 is calculated for one of the blades and then delayed forappropriate time periods before being sent to each of the other blades,in turn.

[0126] For example, on a rotor or propeller with four blades connectedto a hub 350, there is only one control signal generated by thecontroller. This control signal on connection 122 (FIG. 12) drives thealtering-device on the first blade to control the linkage mechanism tothe flap or pitch control 352. The signals which control the other(second, third and fourth) altering-devices 354 356 358 are generated bydelaying the first altering device's control signal by the time that ittakes for the subsequent blades to reach the same angular position inwhich the first blade is currently located. On a four-blade propeller,this delay period would be T/4 where T represents the period of theblade rotation (i.e. the amount of time necessary for a full rotation ofthe rotor).

[0127] The delayed signals are delivered to the altering-devices onconnections 360, 362, and 364 respectively after the appropriate timedelay, (i.e., at time T/4, the signal generated by the controller isdelivered on connection 360 to second altering-device 354, at time T/2,the signal generated by the controller is delivered on connection 362to third altering-device 356 and at time 3/4T, the signal generated bythe controller is delivered on connection 364 to fourth altering-device358). The blades are connected to a mount 370 which is connected to thehelicopter body 380. Note that all blades fly the same around the path,and therefore all the altering-devices (i.e. flaps or propeller blades)should also follow the same prescribed trajectory at each point in therotation of the rotor.

[0128] In the preferred embodiment, the use of blade flap slaving wasbased on the blade tracking requirement imposed on the control system.Blade tracking implies that all blades must fly the same around theazimuth. To achieve the blade tracking, the algorithm was constrained byslaving the blade-flaps to a master blade-flap. It should be noted thatslaving is not an invention but rather a requirement on the controlsystem, and also is not required to insure minimum vibration. Analternate procedure is to have a control system whose dimension is therank of control space for one blade multiplied by the number of blades,i.e. each flap's motion can differ in ways other than a simple timedelay. Vibration and Noise reduction and other constraints such ashelicopter blade tracking can then be imposed through the objectivefunction of the control system. This increases the number of degreesthat are controlled. It is a general principle that if the number ofcontrollable degrees of freedom available to the controller areincreased, then the optimum achieved is a better optimum.

[0129] While the form of the apparatus and method steps herein describedconstitute a preferred embodiment of the present invention, it is to beunderstood that the invention is not limited to this precise form ofeither the apparatus or method disclosed herein and that changes may bemade therein without departing from the scope of the invention which isdefined in the appended claims. Specifically, while the preferredembodiment of the present invention discloses the use of the controllerin a nonlinear system, the controller will work effectively in anapplication in which linear noise and vibration were generated by theassociated plant.

I claim:
 1. A system for actively controlling noise and vibrationproduced by the flow of a fluid over a surface as measured by aplurality of noise and vibration sensors by altering the flow of thefluid, said system comprising: a plant comprising at least a portion ofa plurality of mechanical equipment involved in producing and measuringthe noise and vibration; a preprocessing filtering module which isolatesand quantifies the signals from the sensors at least one of whichsignals represent the measured noise and vibration desired to becontrolled; means for measuring at least one stimulus related to atargeted noise and vibration; a reference signal; an adaptive controllerneural network which receives an input from said reference signal andproduces an adjustment signal; a means for altering the flow of thefluid by the state of said altering means and which receives as inputsaid adjustment signal; an emulator neural network trained to model thedynamics of the fluid flow, said model receiving as input at least oneof said stimulus measurements and the state of said altering means,calculating estimated noise and vibration signals, comparing theestimates with the results from the sensor preprocessing filteringmodule, producing a plant gradient signal representing an error in themodel and feeding back said plant gradient signal into said controllerneural network to adapt said controller neural network; and a pluralityof time-based filters which produces time-based representations of theinputs to said emulator neural network and said controller neuralnetwork.
 2. The system according to claim 1 wherein said emulator neuralnetwork is a feed-forward multi-layer neural network further comprising:an input vector with dimension J comprising at least one of the valuesstored in said time-based filters at the input to said emulator; a firstlayer having a number of neurons equal to (Q+1); an output layer havinga number of neurons L+1 wherein (L+1) is equal to number said noisesensors plus the number said vibration sensors; at least oneintermediate layer having a number of neurons M+1; a plurality ofconnections w_(j,q) ⁽¹⁾ between the input vector and the neurons in thefirst layer, w_(q,m) ⁽²⁾, between the neurons in any successiveintermediate layers, and w_(m,l) ⁽³⁾ between the neurons in the lastintermediate layer and said output layer; a modeling section forcalculating an estimated result from each of the input, output andintermediate nuroeurons a memory section for storing a plurality ofprior estimate results from the neurons in said input layer, said outputlayer and each of said intermediate layers; a performance section forcalculating the error gradient of the plant during a backward pass ofsaid measured noise and vibration; and an update section for updatingthe plant gradients to said controller network.
 3. The system accordingto claim 1 wherein said controller neural network is a feed-forwardmulti-layer neural network comprising: an input vector with dimension Jcomprising at least one of the values stored in said time-based filtersat the input to said controller; a first layer having a number ofneurons (K+1); an output layer having a number of neurons L+1 equal tonumber of means for altering the fluid flow; one or more intermediatelayers having a number of neurons P+1; a plurality of connectionsw_(j,k) ^((1c)) between the input vector and the neurons in the firstlayer, w_(k,p) ^((2c)), between the neurons in any successiveintermediate layers, and w_(p,l) ^((3C)) between the neurons in the lastintermediate layer and said output layer; an objective function sectionfor calculating a desired state of the means for altering the flow ofliquid; a gradient descent performance section for adapting theconnections { w_(j,k) ^((1c)), w_(k,p) ^((2c)), w_(p,l) ^((3c))}according to the plant gradients received from the emulator network; andan update section for updating the state of the controlled alteringmeans to the altering means and to the input of the emulator network. 4.The system according to claim 2 wherein said controller neural networkis a feed forward multi-layer neural network comprising: an input vectorwith dimension J comprising at least one of the values stored in saidtime-based filters at the input to said controller; a first layer havinga number of neurons (K+1); an output layer having a number of neuronsL+1 equal to number of means for altering the fluid flow; one or moreintermediate layers having a number of neurons P+1; a plurality ofconnections w_(j,k) ^((1c)) between the input vector and the neurons inthe first layer, w_(k,p) ^((2c)), between the neurons in any successiveintermediate layers, and w_(p,l) ^((3C)) between the neurons in the lastintermediate layer and said output layer; an objective function sectionfor calculating a desired state of the means for altering the flow ofliquid; a gradient descent performance section for adapting theconnections {w_(j,k) ^((1c)), w_(k,p) ^((2c)), w_(p,l) ^((3c))}according to the plant gradients received from the emulator network; andan update section for updating the state of the controlled alteringmeans to the altering means and to the input of the emulator network. 5.The system according to claim 4 wherein said time-based filter comprisesmemorydelay lines which store the input values for a given time periodand permit access to each of the stored values.
 6. The system accordingto claim 5 wherein said state of altering means input signal to saidemulator networks comprises said adjustment signal from said controllernetwork.
 7. The system according to claim 6 wherein said surface isbeing driven through said fluid.
 8. The system according to claim 7wherein said driven surface is attached to a shaft and drivenrotationally through said fluid.
 9. The system according to claim 8further comprising means for measuring the rotational rate at which saidshaft is driven with said reference signal comprising harmonics of therotational rate at which the surface is driven through the fluid. 10.The system according to claim 9 wherein said harmonics include theharmonic at integer multiples of the rotational rate.
 11. The systemaccording to claim 10 wherein said integer multiples include twice therotational rate.
 12. The system according to claim 8 further comprisingmeans for measuring the axial position of said surface rotating aroundsaid shaft wherein at least one of said stimuli comprises said measuredaxial position of said surface rotating around said shaft.
 13. Thesystem according to claim 8 further comprising means for measuring theaxial position of said surface rotating around said shaft wherein atleast one of said stimuli comprises the sine of said measured axialposition of said surface rotating around said shaft.
 14. The systemaccording to claim 13 further comprising means for measuring the axialposition of said surface rotating around said shaft wherein at least oneof said stimuli comprises said cosine of said measured axial position ofsaid surface rotating around said shaft.
 15. The system according toclaim 14 further comprising means for measuring the forward velocity ofa body attached to said surface through said fluid wherein at least oneof said stimuli comprises said measured forward velocity.
 16. The systemaccording to claim 8 further comprising means for measuring the forwardvelocity of a body attached to said surface through said fluid whereinat least one of said stimuli comprises said measured forward velocity.17. The system according to claim 16 further comprising means formeasuring the axial position of said surface rotating around said shaftwherein at least one of said stimuli comprises said sine of saidmeasured axial position of said surface rotating around said shaft. 18.The system according to claim 16 further comprising means for measuringthe axial position of said surface rotating around said shaft wherein atleast one of said stimuli comprises said cosine of said measured axialposition of said surface rotating around said shaft.
 19. The systemaccording to claim 1 wherein said adaptive controller neural networkreceives said reference signal and at least one of said stimulimeasurements as inputs and produces an adjustment signal.
 20. The systemaccording to 4 wherein said adaptive controller neural network receivessaid reference signal and at least one of said stimuli measurements asinputs and produces an adjustment signal.
 21. The system according toclaim 1 wherein said emulator plant gradient is generated by passing thesignals representing the measured noise and vibration back through theemulator network and calculating the gradients at each layer bybackpropagating the gradients at each layer of the said emulator back tothe input of the neural net controller.
 22. The system according toclaim 21 wherein said controller neural network is adapted with agradient descent algorithm based on said plant gradient produced asoutput by said emulator neural network.
 23. The system according toclaim 32 wherein said preprocessing filtering module comprises abandwidth filter.
 24. The system according to claim 1 wherein saidtime-based filter comprises memory delay lines which store the inputvalues for a given time period and permit access to each of the storedvalues.
 25. The system according to claim 24 wherein said state ofaltering means input signal to said emulator networks comprises saidoutput signal from said controller network for controlling the state ofsaid altering means.
 26. The system according to claim 25 wherein saidsurface is being driven through said fluid.
 27. The system according toclaim 26 wherein said driven surface is attached to a shaft and drivenrotationally through said fluid.
 28. The system according to claim 27further comprising means for measuring the rotational rate at which saidshaft is driven wherein said reference signal is comprised of harmonicsof the rotational rate at which the surface is driven through the fluid.29. The system according to claim 28 wherein said harmonics include theharmonic at integer multiples of the rotational rate.
 30. The systemaccording to claim 29 wherein said integer multiples include twice therotational rate.
 31. The system according to claim 27 further comprisingmeans for measuring the axial position of said surface rotating aroundsaid shaft wherein at least one of said stimuli comprises the axialposition of said surface rotating around said shaft.
 32. The systemaccording to claim 27 further comprising means for measuring the axialposition of said surface rotating around said shaft wherein at least oneof said stimuli comprises said sine of said measured axial position ofsaid surface rotating around said shaft.
 33. The system according toclaim 32 further comprising means for measuring the axial position ofsaid surface rotating around said shaft wherein at least one of saidstimuli comprises said cosine of said measured axial position of saidsurface rotating around said shaft.
 34. The system according to claim 32further comprising means for measuring the forward velocity of a bodyattached to said surface throu gh said f luid wherein at least one ofsaid stimuli comprises said measured forward velocity.
 35. The systemaccording to claim 27 further comprising means for measuring the forwardvelocity of said surface through said fluid wherein at least one of saidstimuli comprises said measured forward velocity.
 36. The systemaccording to claim 27 further comprising means for measuring the forwardvelocity of a body attached to said surface through said fluid whereinat least one of said stimuli comprises said measured forward velocity.37. The system according to claim 36 further comprising means formeasuring the axial position of said surface rotating around said shaftwherein at least one of said stimuli comprises said sine of saidmeasured axial position of said surface rotating around said shaft. 38.The system according to claim 36 further comprising means for measuringthe axial position of said surface rotating around said shaft wherein atleast one of said stimuli comprises said cosine of said measured axialposition of said surface rotating around said shaft.
 39. The systemaccording to claim 1 wherein said preprocessing filtering modulecomprises: means for digitizing the noise and vibration measurements;means for extracting targeted signals within a desired frequencybandwidth from the digitized measurements; means for identifying anenvelope of loci of the targeted signal bandwidth and quantifying asingle discrete value representing that envelope; and means forrate-compressing the loci envelope value;
 40. A system for activelycontrolling the noise and vibration produced by the blades of arotorcraft as measured by a plurality of noise and vibration sensorsmounted on the rotor blades and within the rotorcraft by altering theaerodynamic characteristics of the rotor blades, comprising: apreprocessing filtering module which isolates the measured noise andvibration desired to be controlled; a sensor for measuring therotational rate at which the rotor of the rotorcraft is operating; meansfor determining the angular positions of said blades; a referencesignal; an adaptive feed-forward three-layer controller neural networkadapted to receive as input said reference signal and a positionalvelocity of said rotorcraft and produce an adjustment signal whereinsaid adjustment signal comprises multiple phase and amplitude shiftedharmonics of said reference signal; means for altering the airflow oversaid blades; a feed-forward three-layer emulator neural network trainedto model the dynamics of the blade as well as the results and transferfunction of changes induced by said altering means wherein said modelreceives as input the state of said altering means, the positionalvelocity of said rotorcraft, and the relative positions of said blades,calculates estimated vibration and noise envelope signals, compares theestimated signals with said sensed vibration and noise envelope signals,produces plant gradient signals representing the errors in the emulatormodel of the plant, and feeds back said plant gradient signals to saidcontroller neural network to allow said controller neural network toadapt itself during runtime using a gradient descent algorithm based onsaid plant gradient output of said emulator neural network and saidsensor preprocessing module; and a time-based filter which producestime-based representations of the inputs to said emulator neural networkand said controller neural network comprising delay lines which storethe input values for a given time period and permit access to each ofthe stored values.
 41. The system according to claim 40 wherein saidemulator further comprises: an input vector with dimension J; a firstlayer having a number of neurons (Q+1); an output layer having a numberof neurons (L+1) equal to number of sensors for measuring the targetednoise plus the number of sensors for measuring the targeted vibration;one or more intermediate layers having a number of neurons (M+1); a setof connections w_(j,q) ⁽¹⁾ between the input vector and the neurons inthe first layer, w_(q,m) ⁽²⁾, between the neurons in any successiveintermediate layers, and w_(m,l) ⁽³⁾ between the neurons in the lastintermediate layer and said output layer; a modeling section forcalculating an estimated result from each of the input, output andintermediate neurons; a memory section for storing a plurality of priorestimated results from the neurons in said input layer, said outputlayer and each of said intermediate layers; a performance section forcalculating the gradient of the plant during a backward pass of saidmeasured noise and vibration; and an update section for update the plantgradients to said controller network.
 42. The system according to claim41 wherein said controller further comprises: an input vector withdimension J; a first layer having a number of neurons (K+1); an outputlayer having a number of neurons (L+1) equal to number of means foraltering the fluid flow; one or more intermediate layers having a numberof neurons (P+1); a set of connections W_(j,k) ^((1c)) between the inputvector and the neurons in the input layer and first layer, w_(k,p)^((2c)) between the neurons in any successive intermediate layers, andw_(p,l) ^((3C)) between the neurons in the last intermediate layer andsaid output layer; a objective function section for calculating anadjustment signal; a gradient descent performance section for adaptingthe connections {w_(j,k) ^((1c)), w_(k,p) ^((2c)), w_(p,l) ^((3C))}according to the plant gradients received from the emulator network; andan update section for updating the adjustment signal to the alteringmeans and to the input of the emulator network.
 43. The system accordingto claim 42 wherein said blade position is represented by the sine andcosine of the relative angle of said rotor to the body of saidrotorcraft.
 44. The system according to claim 43 wherein said inputvector to said emulator comprises at least one of said blade positionsstored in said time-based input filter to said emulator.
 45. The systemaccording to claim 44 wherein said input vector to said emulatorcomprises at least one of said positional velocities stored in saidtime-based input filter related to said emulator.
 46. The systemaccording to claim 45 wherein said input vector to said controllercomprises at least one of said positional velocities stored in saidtime-based input filter related to said controller.
 47. The systemaccording to claim 46 wherein said reference signal is comprised ofharmonics of said rotor rotational rate.
 48. The system according toclaim 47 wherein said harmonics include the harmonic at integermultiples of the rotational rate.
 49. The system according to claim 40wherein said input vector to said controller comprises at least one ofsaid positional velocities stored in said time-based input filter tosaid controller.
 50. The process of adaptively controlling noise andvibration comprising the steps of: modeling the plant characteristics ofa noise and vibration creating plant; adjusting the current state of ameans for altering the noise and vibration created by the plant;ascertaining the value of at least one parameter related to the creationof noise and vibration by the plant; creating a reference signal;minimizing an objective function related to a combination of themeasured noise and vibration envelope; creating the plant gradient fromthe model and the objective function to control the state of the meansfor altering the noise and vibration created by the plant; calculatingan estimated noise and vibration envelope based on the parameter valuein conjunction with the state of the altering means; sensing noise andvibration signals; extracting a targeted frequency bandwidth envelope ofnoise and vibration signals desired to be controlled; comparing theestimated noise and vibration envelope with the preprocessed noise andvibration envelope; determining the errors in the plant model based onthe difference in the estimated and preprocessed noise and vibrationenvelopes; and adapting the adjustment parameters to reduce theobjective function.